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Adaptive Search and the Management of Logistic Systems

Base Models for Learning Agents

  • Christian Bierwirth

Part of the Operations Research Computer Science Interfaces Series book series (ORCS, volume 11)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Fundamentals of Evolutionary Adaptive Systems

    1. Front Matter
      Pages 1-1
    2. Christian Bierwirth
      Pages 3-20
    3. Christian Bierwirth
      Pages 21-45
    4. Christian Bierwirth
      Pages 47-59
    5. Christian Bierwirth
      Pages 61-84
    6. Christian Bierwirth
      Pages 85-105
  3. Applications of Evolutionary Adaptive Systems

    1. Front Matter
      Pages 107-107
    2. Christian Bierwirth
      Pages 109-130
    3. Christian Bierwirth
      Pages 131-153
    4. Christian Bierwirth
      Pages 155-177
    5. Christian Bierwirth
      Pages 179-199
  4. Epilogue

    1. Christian Bierwirth
      Pages 201-201
  5. Back Matter
    Pages 203-219

About this book

Introduction

Global competition and growing costumer expectations force indus­ trial enterprises to reorganize their business processes and to support cost-effective customer services. Realizing the potential savings to be gained by exacting customer-delivery processes, logistics is currently sub­ ject to incisive changes. This upheaval aims at making competitive ad­ vantage from logistic services instead of viewing them simply as business necessity. With respect to this focus logistics management comprises the process of planning, implementing, and controlling the efficient, effective flow and storage of goods and services, and related information from point of origin to point of consumption for the purpose of conforming customer requirements I . This definition implies a holistic view on the logistic network, where the actors are suppliers, manufacturers, stock keepers, shipping agents, distributors, retailers and finally consumers. The flow of goods along the supply chain considers raw-materials, work-in-process parts, intermedi­ ate and finished products, and possibly waste. The prevailing manage­ ment of logistics operation is driven by aggregated forecasting of these material flows. Modern logistics management propagates a disaggregated view of the material flow in order to meet the precise requirements at the interface between actors in the supply chain. Replacing aggregated information by detailed values establishes the prerequisites for an integrated process planning which goes for the shift from anticipatory towards response­ based logistic81. Smaller units of goods are considered at shorter periods for planning, implementing and controlling the material flow. From Icf. the Council of Logistics Management (1995).

Keywords

algorithms evolutionary algorithm genetic algorithms logistics scheduling

Authors and affiliations

  • Christian Bierwirth
    • 1
  1. 1.University of BremenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-8742-6
  • Copyright Information Kluwer Academic Publishers 2000
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-4679-1
  • Online ISBN 978-1-4419-8742-6
  • Series Print ISSN 1387-666X
  • Buy this book on publisher's site